Ground control points (GCP's) are used in orthorectifying satellite, aerial or aero survey imagery to standard map projections. A ground control point can be any point on the surface of the earth which is recognizable on remotely sensed images, maps or aerial photographs and which can be accurately located on each of these. A ground control point has defined associated coordinates in a coordinate reference systems A ground control point is a point on the surface of the earth of known location (i.e. fixed within an established co-ordinate reference system). GCP's are used to geo-reference image data sources, such as remotely sensed images or scanned maps, and divorced survey grids, such as those generated during geophysical survey. A GCP could be:                a copy of a part of a paper map showing a selected point and its surrounding;        an image chip from a scanned map showing a selected point and its surrounding;        an image chip from a digital map showing a selected point and its surrounding;        a written description or sketch of the selected point        an image from an aerial/satellite or ground based photo showing a selected point and its surrounding; or        any other representation of a specific location suitable documented so as to be recognizable in an aerial/satellite image or planimetric map.        
A GCP can be any photo-recognizable feature to identify one point having associated precise X, Y and Z coordinates in a coordinate reference system. A GCP describes an earth surface feature which is clearly identifiable in a satellite or aerial imagery. The most significant requirement for a GCP is it's visibility in the image to be orthorectified. A secondary characteristic is that it be durable. A GCP should ideally have a size which is at least 4 times the size of a pixel in the image to be orthorectified. Earth surface features used for defining GCP's can be cultural features, line features and natural features.
A cultural (man made) feature is usually the best point to use as GCP. It covers road intersections, road and rail road intersections, road and visible biogeographic boundary intersections, such as the intersection of a road and the boundary line between a forest and an agricultural field, intersections, river bridges, large low buildings (hangars, industrial buildings, etc), airports, etcetera.
In present application line features could be used when they have well defined edges in the imagery. The GCP is normally selected as a center of the intersection of two line features. The two line features forming the intersection have to cross with an angle larger than 60 degrees.
Natural features are generally not preferred because of their irregular shapes. It may however be necessary to use natural features in areas lacking suitable cultural features. If a natural feature has well defined edges, it may be used as a GCP. It could be forest boundaries forest paths, forest clearings, river confluence, etc. When selecting such points it must to be taken into account that certain boundaries can be subject to variations (forest, water bodies) in time. In situations where there are insufficient suitable features, it is possible for the surveyor to create an observable feature for the purpose of identifying a GCP.
To geo-reference or rectify aerial or satellite imagery, a set of GCP's has to be selected for each image. The GCP's of a set should be uniformly selected in the image. Points near the edges of an image should be selected and preferably with even distribution in the image. The set of GCP's should preferably also respect terrain variations in the scene, i.e. select point at both highest and lowest elevations.
GCP's could be generated by a human going into the field and gathering both an image or corresponding description of the GCP and the corresponding X, Y and Z coordinate in a coordinate reference system by a position determination means of for example a GPS receiver. In “Accurate mapping of Ground Control Point for Image Rectification and Holistic Planned Grazing Preparation” by Jed Gregory, et al., GIS Training and Research Center, Idaho State University Pocatello, ID 83209-8130, October 2006, GCP's had to be established and their exact spatial location recorded to ensure accurate georectification of the imagery. Ten GCP's were setup strategically throughout the area to be georectified. The GCP's were setup using two strips of plastic, six inches wide and six feet long, laid across each other in the shape of a cross (+). All GCP's were oriented with each arm of the cross pointing in one of the four cardinal directions (north, south, east, west). After placement of each GCP a GPS location was recorded at the center of the cross using a Trimble GeoXT GPS unit. Said document makes clear the huge amount of time and effort that is necessary to collect accurate GCP's.
There are basically two corrections that are made in an orthorectification process. Orthorectification is the transformation of a perspective view image into an image wherein each pixel has a known XY-position on the geoid describing the earth surface and wherein each pixel is regarded to be viewed perpendicular to the earth surface in said XY-position. First, any shifts (translation and rotation errors) tilts or scale problems can be corrected and second the distortion effects of elevation changes can be corrected. In current orthorectification processes applied to images, elevation distortion is the major cause of horizontal errors. This is illustrated in FIG. 1. A camera mounted in an aircraft 1 records perspective view images of the earth surface 2 (shown here in profile). However, only one pixel in the image can be representing an orthogonal view of the earth surface and the other pixels are all angled view representations of the earth surface. FIG. 1 shows a profile of the earth surface for a given y coordinate. Horizontal line 3 is assumed to represent a profile of a reference surface of the earth for the given y coordinate in a coordinate reference system, for example WGS84 or any other geoid describing the earth surface in a coordinate reference system. Shown is a building structure 4, for example a bridge, on the earth surface whose xyz position on the earth surface 2 and height are known. Furthermore, the position and orientation in the coordinate reference system of the capturing point 5 of the aerial image is known (for example by means of accurate GPS and/or other position/orientation determination means). By means of geometry, it is possible to determine the pixels of the upper side of the building structure and to determine the corresponding x,y position. However, if the height, i.e. z coordinate, of the earth surface with respect to the reference surface 3 is not known, a first terrain-induced error 6 will be introduced in the orthorectified image. Similarly, if also the height of the building structure is not known an additional building height-induced error 7 will be introduced in the final orthorectified image. In that case the upper side or the building structure can be projected meters aside the correct xy position in the orthorectified image. In case the building structure is a bridge, the road on the bridge will be projected erroneously if the elevation information with respect to the reference surface is not (accurately) known. FIG. 2 illustrates this type of error.
FIG. 2 shows an orthorectified image wherein a digital elevation model (DEM) is used to orthorectify the aerial image. A DEM, or “bare earth”, which it is often referred to as, is created by digitally removing all of the cultural and bio-geographic features inherent to a digital surface model DSM by exposing the underlying terrain. A DSM is a first surface view of the earth containing both location and elevation information in a coordinate reference system. A DEM can be represented as a raster (a grid of squares), sets of iso-lines or contours, or as a triangular irregular mesh network. The USGS 10 m National Elevation Data Set (NED) is a cost-effective DEM available but fails to allow for accurate orthorectification for bridges, buildings and elevated structures as shown in FIG. 2. By not taking into account the height of the bridges, the upper sides of the bridges are shifted with respect to the real location of the bridges. The real location of the bridges in FIG. 2 are indicated by the white lines superimposed on the orthorectified image. FIG. 3 shows an orthorectified image wherein an accurately geo-coded DSM is used to orthorectify the aerial image. In can be seen that by using the correct heights of the building structures, the building structures are correctly projected on the orthorectified image space. The building structures are correctly projected when the white lines indicating the outlines of the building structures coincide with the visual outlines in the orthorectified image.
It should be noted that both DEMs and DSMs provides only a model of the earth surface. They do not comprise information which is easily recognizable on sensed images, maps and aerial photographs. Without GCP's associated with a DEM or DSM, they cannot be used to orthorectify such images. The accuracy of the GCP's used and the number of GCP's (count) and distribution/density across the image to be rectified will determine the accuracy of the resultant image or orthorectification process. The characteristic of the underlying elevation changes determines the required distribution/density of GCP's. For example a flat part of Kansas needs only some GCP's at the edges of the flat part. A small bridge over a little river doesn't need much. A giant bride over a massive ravine may need a high density to describe correctly the edges of the bridge. Likewise rolling hills will need more than a flat tilt.
Geographic Information Systems often combine both digital map information and orthorectified images in one view. Information from the image can be extracted or analyzed to add to, correct or validate the digital map information. Similarly, orthorectified images could be used to extract digital map information for use in a navigation device. In both situations it is important that the location of features in the orthorectified images correspond to their real locations on the earth. In the first case, due to incorrect heights, the position of road surfaces in the orthorectified image does not coincide with the corresponding road surfaces from the digital map. For an example see FIG. 2. In this case, the navigation device could measure positions that are different from those in its map database that were extracted from the poorly orthorectified image and could provide an alarm erroneously informing the user of the navigation device about unsafe driving conditions.
A requirement for generating a correct orthorectified image from an aerial image or satellite image is that sufficient GCP's are present within the area represented by the orthorectified image. Nowadays, the costs of orthorectification increase linearly with the amount of GCP's to be captured by humans. The more, GCP's are needed to obtain the required accuracy of an orthorectified image, the more human effort is needed.
There is a lack of cheap, accurate (with known accuracy) and well distributed ground control points to help control positionally accurate navigation and mapping applications. Furthermore, Advanced Driver Assistance Systems (ADAS) require accurate 3D positional information about the road to control such systems. This requires a very dense network of GCP's along the road surface to be able to rectify aerial or satellite imagery sufficient accurately. For these applications it is important that the road surface is correctly positioned in the orthorectified image. To be able to do this, elevation information is needed about the road surface, especially the elevation information of bridges, banks, elevated highways and flyovers.
The current state of ground control products for calibration and rectification of geospatial imagery is patchy and inconsistent in almost all areas of the globe. The following data sources exist for calibration and rectification of geospatial data:
a) DEM/DTM data derived from government topographic datasets. However, these data are frequently coarse and out of date. In addition they vary greatly in quality from region to region;
b) DEM/DTM derived from airborne/satellite radar platforms. These are expensive and often cover large swaths of area that may not be of interest to many commercial mapping entities. These still require positional calibration from an independent accurate source. Satellite platforms currently do not provide data that consistently meet the precision requirement for ADAS-level work;
c) High quality survey grade GPS ground control points. These are expensive on a per point basis and require special permission for acquisition in some countries. Furthermore, the opportunities for repeatability are minimal;
d) Low quality GPS ground control points (ad hoc/non-survey grade). These are often not photo-identifiable and may be subject to rapid obsolescence. Geodetic metadata may be inconsistent and ill-defined. Furthermore, the location of points is generally not well planned;
e) GPS “track lines” from vehicles. These are almost not photo-identifiable and do not provide an accuracy that is higher than carriageway width. First, they are difficult to correlate with other track lines and will give different positions based upon subtle driving patters especially at intersections, making correlating transportation nodes impossible;
f) Existing Aerial Image Products. These may be of utility for validating/rectifying lower quality output. But in production of Geospatial data, these are not suitable. In addition these suffer from a host of localized errors which are not easy to detect in 2D images; and
g) Existing government or commercial centerline maps. These maps are abstract modeling specifications or centerline data. The accuracy profiles of such data sets are inconsistent and they lack quality elevation data.
There is need for a geodetic reference database product, that comprises sufficient GCP's or ground control information to orthorectify aerial or satellite imagery with enough accuracy in three dimensions to use the product as a reliable data source for GIS applications at least as it applies to the surface of roads.